1. Field of the Invention
The specification relates to person detection systems. In particular, the specification relates to a system and method for detecting a person through a leg connectivity test based at least in part on depth data from a sensor and estimating pose information for the detected person.
2. Description of the Background Art
Today many computer systems and machines rely on person detection techniques. Under many situations, machines and computer systems need to know if there is a human present at a particular location in order to turn on/off or activate a particular program if a human or humans are present or absent. Therefore, the machines and computer systems are often equipped with a particular sensor for measuring surrounding objects and loaded with a program for processing data from the sensor to detect whether a human is present. For example, a surveillance system is equipped with a consumer camera. The surveillance system can automatically adjust the focus of the consumer camera when detecting humans and further identify the humans. Examples for such machines and computer systems can also include an automobile safety system that monitors a driver's status; and a robotic application that needs an accurate detection and pose estimation of humans. These machines and computer systems apply drastically different methods based on different types of sensors equipped.
Among these machines and computer systems, there are several of them designed to detect one or multiple persons based on a depth image. The existing method is to utilize red-green-blue color and depth (“RGB-D”) information of pixels composing the image to fit a skeleton model of a human. This method often requires the visibility of the whole body of a human. Therefore, one of the main problems of the existing method is that this method becomes inapplicable if a human's body is not entirely visible because of occlusions or because of the sensor's limited field of view. Especially, when the sensor is placed at a low position, the existing machines and computer systems are unable to detect the position of the human and thus unable to calculate pose information for the human.
Another main problem of the existing methods is that the existing methods often require a training session or prior knowledge before being able to detect persons and estimate pose information for the persons.